class_docstr: Tests for DP-enabled DNNClassifier.
code: def testDNN(self, classes):
      train_features, train_labels = test_utils.make_input_data(
          256, classes
      )
      feature_columns = []
      for key in train_features:
          feature_columns.append(
              tf.feature_column.numeric_column(key=key)
          )
  
      optimizer = functools.partial(
          DPKerasSGDOptimizer,
          learning_rate=0.5,
          l2_norm_clip=1.0,
          noise_multiplier=0.0,
          num_microbatches=1,
      )
  
      classifier = dnn.DNNClassifier(
          hidden_units=[10],
          activation_fn="relu",
          feature_columns=feature_columns,
          n_classes=classes,
          optimizer=optimizer,
          loss_reduction=tf.losses.Reduction.NONE,
      )
  
      classifier.train(
          input_fn=test_utils.make_input_fn(
              train_features, train_labels, True, 16
          )
      )
  
      test_features, test_labels = test_utils.make_input_data(
          64, classes
      )
      classifier.evaluate(
          input_fn=test_utils.make_input_fn(
              test_features, test_labels, False, 16
          )
      )
  
      predict_features, predict_labels = test_utils.make_input_data(
          64, classes
      )
      classifier.predict(
          input_fn=test_utils.make_input_fn(
              predict_features, predict_labels, False
          )
      )
  
file_path: privacy/tensorflow_privacy/privacy/estimators/dnn_test.py
imports: import functools
  from absl.testing import parameterized
  import tensorflow
  from tensorflow_privacy.privacy.estimators import dnn
  from tensorflow_privacy.privacy.estimators import test_utils
  from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasSGDOptimizer
label_desc: Use image node_id 1 for calling the DPDNNClassifierTest obj's underlying member method code with example usage: obj.testDNN(classes) without return types